We present a novel method for predicting the secondary structure of a protein from its amino acid sequence. Most existing methods predict each position in turn based on a local window of residues, sliding this window along the length of the sequence. In contrast, we develop a probabilistic model of protein sequence/structure relationships in terms of structural segments, and formulate secondary...

Most current methods for prediction of protein secondary structure use a small window of the protein sequence to predict the structure of the central amino acid. We describe a new method for prediction of the non-local structure called-sheet, which consists of two or more-strands that are connected by hydrogen bonds. Since-strands are often widely separated in the protein chain, a network with ...

The influence of long-range residue interactions on defining secondary structure in a protein has long been discussed and is often cited as the current limitation to accurate secondary structure prediction. There are several experimental examples where a local sequence alone is not sufficient to determine its secondary structure, but a comprehensive survey on a large data set has not yet been d...

The influence of long-range residue interactions on defining secondary structure in a protein has long been discussed and is often cited as the current limitation to accurate secondary structure prediction. There are several experimental examples where a local sequence alone is not sufficient to determine its secondary structure, but a comprehensive survey on a large data set has not yet been d...

Computationally biology is the innovative research for better drug designing. A number of classifiers and techniques are used for prediction of secondary structure prediction of proteins. The basic aim of this paper shows the comparative study by using these three models: Artificial Neural Network, Fuzzy Logic, and Hidden Markov Model and to acquire the optimum end result.

Bidirectional recurrent neural network(BRNN) is a noncausal system that captures both upstream and downstream information for protein secondary structure prediction. Due to the problem of vanishing gradients, the BRNN can not learn remote information efficiently. To limit this problem, we propose segmented memory recurrent neural network(SMRNN) and use SMRNNs to replace the standard RNNs in BRN...

l/sing a new database of 20 proteins not included in any of the previously used training datasets, we have incorporated multiple alignment information from homologous proteins into two well-characterized prediction methods: COMBINE (a jury method) and the Q-L (or quadratic-logistic) method. It is found that the increase in accuracy from the use of related proteins is similar for both methods (5...

Secondary structure of proteins refers to local and repetitive conformations, such as α-helices and β-strands, which occur in protein structures. Computational prediction of secondary structure from protein sequences has a long history with three generations of predictive methods. This unit summarizes several recent third-generation predictors. We discuss their inputs and outputs, availability,...

A new method for predicting protein secondary structure from amino acid sequence has been developed. The method is based on multiple sequence alignment of the query sequence with all other sequences with known structure from the protein data bank (PDB) by using BLAST. The fragments of the alignments belonging to proteins from the PBD are then used for further analysis. We have studied various s...

Computational sequence design methods are used to engineer proteins with desired properties such as increased thermal stability and novel function. In addition, these algorithms can be used to identify an envelope of sequences that may be compatible with a particular protein fold topology. In this regard, we hypothesized that sequence-property prediction, specifically secondary structure, could...